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Enhancement of confocal microscopy images using Mueller‐matrix polarimetry

2009· article· en· W2077823552 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Microscopy · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMueller calculusPolarimetryConfocalOpticsSpecular reflectionMaterials scienceComputer sciencePhysicsArtificial intelligenceComputer visionScattering

Abstract

fetched live from OpenAlex

A simplified procedure based on Mueller-matrix polarimetry has recently been reported as a method of retinal image improvement in a confocal ophthalmoscope [J. M. Bueno et al., J. Opt. Soc. Am. A 24, 1337 (2007)]. Here, we have applied the technique to imaging static samples providing well-defined reflection properties. The method uses a generator of polarization states in the illumination pathway of a confocal scanning laser system. From the calculated four elements of the Mueller matrix of any sample and instrument combination, the best images defined by different metrics were constructed. For samples with specular, diffuse and mixed reflections, the best-constructed images showed an enhancement in both objective and subjective image quality compared to the original images and those obtained from frame averaging. This technique could improve microscopic imaging in many diverse fields, particularly in biomedical imaging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.297
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it